The Role of Uncertainty, Awareness, and Trust in Visual Analytics

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A Review of User Interface Design for Interactive Machine Learning (2018, background): “The literature suggests that there is likely to be a close relationship between user tolerance of error and the level of clarity in system uncertainty (Sacha et al. 2016).”
Towards better analysis of machine learning models: A visual analytics perspective (2017, background): “ainties. One interesting direction for futureresearchis to develop visual analytics techniquesthateectively measure and quantify the uncertainty in data processing, model building, and visualization [50] and help experts quickly identify the potential issues in a machine learning model of interest. ”
A review of volunteered geographic information quality assessment methods (2017, background): “Furthermore, the works of Sacha et al. (2016), where they introduce a framework that integrates trust and other various quality indicators in a knowledge generation process within the visual analytics paradigm can be adapted in future research to assess and visually analyze quality of VGI.”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “system are as follows: G5: Model Visualization and Inspection: Similar to AI novices, data experts also benefit from machine learning interpretability to inspect model uncertainty and trustworthiness [181]. For instance, machine-learning explanations help data experts to visualize models [86] and in”